Geoscience Data Journal最新文献

Local ensemble transform Kalman filter (LETKF)-based Ocean Research Analysis (LORA) version 1.0 datasets for western North Pacific (WNP) and Maritime Continent (MC) regions (LORA-WNP and -MC, respectively) are released through the JAXA-RIKEN Ocean Analysis website. The LORA datasets are created using an eddy-resolving LETKF-based ocean data assimilation system with satellite sea-surface temperature, salinity, and height data and with in-situ temperature and salinity data assimilated daily. The LORA datasets include 128-member ensemble analyses at the sea surface (2D), each term of mixed-layer temperature and salinity budget equations, and the related variables (2D) such as mixed-layer depth and heat and freshwater fluxes as well as system grid information and analysis ensemble mean and spread (3D), from August 2015 to January 2024 (as of June 2024). The LORA datasets are useful for geoscience research and practical applications, especially for particle tracking, boundary conditions of atmospheric models, and research on spatiotemporal variations in sea-surface temperature and salinity.

Geomorphic environments play a crucial role in influencing the preservation and characteristics of tsunami deposits. This paper introduces a global database of tsunami deposits, encompassing information on deposit locations, thematic data such as geomorphic environments and proxies and bibliographic details. Additionally, the database features maps incorporating environmental parameters and the precise locations of tsunami deposits. The primary utility of this database lies in assessing progress and identifying gaps in knowledge. It also involves analysing the relationship between environmental parameters and interpreting areas with varying probabilities of tsunami deposit preservation. The files are readily compatible with GIS software and can seamlessly integrate into spatial databases associated with tsunamis or other hazards. This contributes significantly to disaster risk management, enhancing preparedness and response efforts by providing a comprehensive historical dataset on tsunamis. Future applications of the database include the incorporation of modern deposits, boulders and new data from paleotsunami and historical studies. By enhancing data with thematic information, such as dating techniques and creating timelines, the database facilitates a more comprehensive understanding. The correlation between geomorphic environments and proxies aids in selecting sampling sites and identifying suitable proxies for analysis. Encouraging an open-access approach, this database invites all interested researchers to include and modify additional information. The information compiled for this database serves multiple purposes: (1) assessing the global distribution of tsunami deposits; (2) identifying knowledge gaps in tsunami deposits; (3) guiding the selection of study areas for further research and finally; (4) enabling a meta-analysis of the information gathered.

Phenology reflects the life cycle of vegetation, crucial for monitoring global vegetation diversity, ecosystem stability, and agricultural security. However, there is currently no dataset related to phenology. The 24 solar terms (24STs), based on the Sun's annual motion, reflect the changing seasons, temperature fluctuations, and phenological phenomena. They serve as a vital means to characterize vegetation phenology. This study generate a global Normalized Difference Vegetation Index (NDVI) product based on 24STs using Moderate Resolution Imaging Spectroradiometer (MODIS) on the Google Earth Engine (GEE). The 24STs NDVI dataset adopted the maximum value compositing (MVC) to process the NDVI values between two adjacent 24STs. The product has a spatial resolution of 250 m, covering the period from 2001 to 2022. Comparing with the MOD13Q1, good spatiotemporal consistency between the two datasets was observed, confirming the reliability of the 24STs product. However, the 24STs product holds distinct phenological meanings. This product introduces, for the first time, a vegetation index dataset based on the 24STs, enriching the vegetation index dataset and facilitating further research on phenology.

Maritime historical documentary sources of weather and state of sea surface including sea ice can aid in filling a known climate knowledge gap for the Southern Ocean and Antarctica for the first half of the 20th century. This study presents a data set of marine climate, sea ice and icebergs recovered from a collection of logbooks from mainly Norwegian whaling factory ships that operated in the Southern Ocean during 1929–1940. The data set comprises some 8000 weather and 4000 sea ice/open sea records from austral summers of the study period. This paper further discusses the structure and content of most common Norwegian maritime documentary sources of the period along with the practices of logging information relevant for the study, such as time keeping, positioning and making weather observations. An emphasis was made on recovery of notes on sea ice and icebergs and their interpretation in terms of WMO categories of sea ice concentration. Data, including ship-related metadata from all individual documents are homogenized and structured to a common machine-readable format that simplifies its ingestion into relevant climate data depositories.

The seismic-electromagnetic phenomenon entails the generation of transient electromagnetic signals, which can be observed both simultaneously (co-seismic) and preceding (pre-seismic) a seismic wave arrival. Following the most accredited hypothesis, these signals are mainly due to electrokinetic effects, generated on microscopic scale in porous media containing electrolytic fluids. Thus, the seismic-electromagnetic signals are expected to be suitable for the detection and tracking of crustal fluids. Despite the growing interest in this phenomenon, there is a lack of freely available observational database of earthquake-related electromagnetic signals recorded at co-located seismic and magnetotelluric stations. To fill this gap, we set up two multicomponent monitoring stations in two seismically active areas of Southern Italy: the Gargano Promontory and the High Agri Valley. This work is both aimed to systematically analyse earthquake-generated seismic-electromagnetic recordings and to make the collected database accessible to the scientific community.

High-resolution climate projections are valuable resources for understanding the regional impacts of climate change and developing appropriate adaptation/mitigation strategies. In this study, we developed a 10-km gridded hydrometeorological dataset over India by dynamic downscaling of the bias-corrected Community Earth System Model (CESMv1) climate projections under RCP8.5 scenario using the state-of-the-art Weather Research and Forecasting (WRF) model. The downscaled CESM dataset (DSCESM) is archived in the World Data Center for Climate (WDCC) portal at three temporal resolutions (daily, monthly and monthly climatology) for current (2006–2015), mid-century (2041–2050) and end-century (2091–2100) periods. The dataset includes 2-m air temperature, total accumulated precipitation, wind speed, relative humidity, sensible and latent heat fluxes, along with surface shortwave and outgoing longwave radiation. All the DSCESM variables were evaluated against reanalysis data and station observations for the period 2006–2015. This dataset can help us quantitatively understand regional climate change in India. It can also be used in conjunction with agricultural, hydrological, fire and other application models for climate change impact assessment on various sectors to help develop effective adaptation/mitigation strategies.

Canada-wide, statistically downscaled simulations of global climate models from the Sixth Coupled Model Inter-comparison Project (CMIP6) have been made available for 26 models using a new multivariate approach and an improved observational target dataset. These new downscaled scenarios comprise daily simulations of precipitation, maximum temperature, and minimum temperature at 1/12° resolution across Canada. Simulations from each of the 26 downscaled global climate models span a historical period (1950–2014), and three future Shared Socio-economic Pathways (SSPs) representing low (SSP1 2.6), moderate (SSP2 4.5) and high (SSP5 8.5) future emissions from 2015 to 2100. Results from an evaluation of the multivariate downscaling method over Canada yield improved performance in replicating multivariate and compound climate indices compared to previously used univariate downscaling methods. This Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS-M6) dataset is intended to facilitate climate impacts assessments, hydrologic modelling, and analysis tools for presenting climate projections.

We installed a meteorological recording system at Badwater (elev. −75 m), the lowest point in Death Valley, California and recorded data over the period 1998–2019. A second station (the Outhouse Station) was established nearby from 2014 to 2019. Here, we report on and publicly archive the data from these two stations. Of interest was the comparison between two air temperature measurements at the Badwater Station, the first with an aspirated platinum resistance temperature device and the second with a thermistor probe in a passive sun shield. During the hottest periods of the summer when temperatures were typically between 30°C at night and 50°C daily peak, the passively shielded sensor indicated up to 0.5°C warmer than the aspirated temperature sensor due to radiative effects. The data suggest a correction for radiative heating of (T–35)/30, for T > 35°C, where, T, is the uncorrected temperature reading of a passively shielded sensor subtracted after any calibration at lower temperatures. Our station was the first precision temperature measurements at Badwater. A longer record exists for the reporting station near the visitor's centre at the Furnace Creek. The summer temperature maxima at the Badwater site correlate well with the values the same day from the Furnace Creek site. The daily maximum temperatures in winter at the Badwater site appear to be about 1°C lower than at the Furnace Creek site. The largest differences are in the minimum temperatures for which the Badwater site averages about 2–3°C warmer than the Furnace Creek site.

The regional climate of New Zealand's South Island is shaped by the interaction of the Southern Hemisphere westerlies with the complex orography of the Southern Alps. Due to its isolated geographical setting in the south-west Pacific, the influence of the surrounding oceans on the atmospheric circulation is strong. Therefore, variations in sea surface temperature (SST) impact various spatial and temporal scales and are statistically detectable down to temperature anomalies and glacier mass changes in the high mountains of the Southern Alps. To enable future studies on the processes that govern the link between large-scale SST and local-scale high-mountain climate, we utilized dynamical downscaling with the Weather Research and Forecasting (WRF) model to produce a regional atmospheric modelling dataset for the South Island of New Zealand over a 16-year period between 2005 and 2020. The 2 km horizontal resolution ensures realistic representation of high-mountain topography and glaciers, as well as explicit simulation of convection. The dataset is extensively evaluated against observations, including weather station and satellite data, on both regional (in the inner domain) and local (on Brewster Glacier in the Southern Alps) scales. Variability in both atmospheric water content and near-surface meteorological conditions is well captured, with minor seasonal and spatial biases. The local high-mountain climate at Brewster Glacier, where land use and topographic model settings have been optimized, yields remarkable accuracy on both monthly and daily time scales. The data provide a valuable resource to researchers from various disciplines studying the local and regional impacts of climate variability on society, economies and ecosystems in New Zealand. The model output from the highest resolution model domain is available for download in daily temporal resolution from a public repository at the German Climate Computation Center (DKRZ) in Hamburg, Germany (Kropač et al., 2023; 16-year WRF simulation for the Southern Alps of New Zealand, World Data Center for Climate (WDCC) at DKRZ [data set]. https://doi.org/10.26050/WDCC/NZ-PROXY_16yrWRF).

The data article describes the Road Damage Dataset, RDD2022, encompassing of 47,420 road images from majorly six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The dataset incorporates over 55,000 instances of road damage, specifically longitudinal cracks, transverse cracks, alligator cracks, and potholes. Designed to facilitate the development of deep learning methodologies for automated road damage detection and classification, RDD2022 was unveiled as part of the Crowd sensing-based Road Damage Detection Challenge (CRDDC'2022), with a major contribution from the challenge winners. This challenge garnered global participation, urging researchers to propose solutions for automatic road damage detection in multiple countries. A noteworthy outcome of CRDDC'2022 was the emergence of a top-performing model achieving a remarkable F1 Score of 76.9% for road damage detection in all six countries using RDD2022. This success underscores the dataset's practical applicability for municipalities and road agencies, enabling low-cost, automatic monitoring of road conditions. Beyond its immediate utility, RDD2022 stands as a valuable benchmark for researchers in computer vision, geoscience, and machine learning, offering a rich resource for algorithmic evaluation in diverse image-based applications, including classification and object detection. The latest big data cup, Optimized Road Damage Detection Challenge (ORDDC'2024), is also based on RDD2022, underscoring its continued relevance and pivotal role in current research and development endeavors.